The accuracy of groundwater potential assessment depended on the selection of influencing factors. The groundwater influencing factors are selected based on the nature of the study area, considering those with the most significant impact on groundwater recharge. In this process, several elements influencing the presence of groundwater are represented by thematic layers, which are geomorphology, Land use land cover (LULC), geology, soil, rainfall, drainage density, lineament density, proximity to stream, proximity to sea, topographic wetness index (TWI), topographic position index (TPI), slope, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI).

The Geological Survey of India (GSI) contributed geomorphological and geological data. The National Bureau of Soil Survey and Land Use Planning (NBSS & LUP) offered Soil data. Specifically, geology, Geomorphology, and soil maps were used at a 1:50,000 scale. In the present investigation, the Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were extracted from Sentinel-2B-2A imagery. Specifically, we utilized a dataset acquired on 16 September 2024 (late southwest monsoon/early northeast monsoon), chosen for minimal cloud cover and representative hydrological conditions was computed from Band 8 (NIR) and Band 4 (Red), while NDWI was derived from Band 8 (NIR) and Band 3 (Green), all of which have a 10 m spatial resolution. LULC mapping was performed using Supervised maximum likelihood algorithms, and accuracy assessment was conducted with reference samples (Google Earth and field verification). Rainfall and groundwater level data were sourced from the State Ground and Surface Water Resources Data Center, Tamil Nadu. Topographic and hydrological parameters, including lineament density, Drainage density, Slope, TWI, TPI, Proximity to stream, and Proximity to sea, were extracted from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) at a 30-meter resolution. All the data are collected from various sources (Table 1) and processed in ArcGIS 10.8 software. Interpolation techniques were adopted to formulate continuous spatial surfaces from distinct point measurements, thus supporting the accurate estimation of parameter values in sites that had not been sampled. Among the various methods evaluated, Inverse Distance Weighted was chosen due to its exceptional statistical efficacy with the lowest RMSE and an excellent R² accuracy. Figure 2 illustrates the comprehensive methodological framework utilized for calculating the Groundwater Potential Index (GWPI).

The Analytic Hierarchy Process (AHP), developed by Thomas L. Saaty (1980), is a decision-making framework that helps to solve complex problems in a clear hierarchical model. It applies pairwise comparison to quantify the relative importance of criteria using a standard scale. AHP also calculates consistency ratios to ensure the expert judgments remain logically sound and reliable (Saaty, 1987). AHP offers advantages such as transparency and simplicity, effectively integrating quantitative and qualitative criteria in water resources management (Cabrera et al., 2011). The Analytic Hierarchy Process (AHP) provides a versatile framework that facilitates the modification of expert assessment, affording individuals the most challenging and intricate issues (Galo, 2017).

The Analytical Hierarchy Process (AHP) is a coherent approach utilized for the organization and scrutiny of complex decision-making environments, and it is broadly implemented in groundwater potential evaluation. The determination and ranking of parameters and sub-parameters in the AHP necessitate a coherent framework that incorporates expert evaluation, thematic layers, and geospatial analysis (Anteneh et al., 2022). The specification of parameters is commonly guided by specialist perspectives and extensive literature assessments, thereby affirming that the most relevant elements are acknowledged for the designated research area (Osinowo and Arowoogun, 2020). The AHP methodology utilizes Saaty's nine-point scale to allocate weights to each parameter according to its relative significance, as displayed in Table 2 (Hayer et al., 2023). This scale facilitates a pairwise comparison of parameters, thereby promoting a structured decision-making paradigm. Following the assignment of initial weights, the values undergo normalization to ensure that they collectively equal one (Ravindran et al., 2024). This procedure is instrumental in preserving consistency and comparability across various parameters. The weights are derived by averaging the normalized values corresponding to each row, yielding a priority vector that conveys the relative importance of each criterion (Jarrah et al., 2022). These weights equate to unity as they are extracted from a normalized matrix, thereby guaranteeing that cumulative importance is allocated across all criteria (Szabo et al., 2021). The final weights exemplify the relative importance of each criterion within the decision-making framework, thus enabling a clear prioritization of alternatives (Howari et al., 2023). Applied importance based on the Saaty scale in the pairwise matrix for the AHP is displayed in Table 3.

The Consistency Ratio (CR), a construction first articulated by Saaty in 1980, serves as a metric for evaluating the coherence in the allocation of weight attributed to various parameters. This ratio facilitates a quantitative evaluation of weights derived from the normalized pairwise comparison matrix (NPCM) delineated in Table 4. If the CR fall below 0.10 (10%), it signifies a substantial degree of precision; in contrast, if the CR surpasses 0.10, it may necessitate a re-evaluation of the judgments to secure enhanced accuracy (Saaty, 1977).

Where CR = Consistency Ratio (CR)

CI = Consistency Index (CI), and RI = Random Consistency Index (RI)

λ_max = largest eigenvalue and N = Number of parameters.

A commonly applied technique for identifying Groundwater Potential Zones (GWPZ) in the realm of Geographic Information Systems (GIS) is the Weighted Overlay Analysis (WOA) (Thanh et al., 2022; Jebaraj and Rajagopal, 2024). WOA represents a multi-criteria decision-making (MCDM) approach that synthesizes diverse thematic layers, each assigned weights that accurately represent their respective impacts on groundwater potential (Berhanu and Hatiye, 2020). This methodology facilitates spatial decision-making by systematically and transparently amalgamating standardized raster inputs.

Weights are allocated to each variable following their comparative significance to the prevalence of groundwater occurrence (Kandakoglu et al., 2019). Those weights can be allocated through expert assessments, comprehensive literature evaluations, or applied through a systematic AHP technique (Aladejana et al., 2016). The weights for fourteen criteria and their sub-criteria were incorporated into thematic layers in ArcGIS. The groundwater potential map was produced through weighted overlay analysis in GIS. This process involved computing the weighted sum of thematic layers as per Equation 3.

Where Wi is denoted as the weightage of each parameter, and Xi refers to the rank of each thematic layer. The raster output layer from the Weighted Overlay Analysis (WOA) is classified into five categories: very low, low, moderate, and very high. This classification is based on the identification of regions with varying groundwater potential yield in the research area.

Assessment of geomorphology elucidates the genesis, distribution, and dynamics of groundwater (Letz et al., 2021). In geomorphological formations, the presence of groundwater is predominantly shaped by lithological characteristics, gradient, drainage configuration, infiltration capacity, and hydrological runoff (Arulbalaji et al., 2019; Pandey and Purohit, 2022). Based on the geomorphological attributes of the research area (Figure 3a) includes a deltaic plain, floodplains, a coastal plain, a salt pan, and water bodies. The deltaic plain, which constitutes the predominant portion of the region (77.66%), displays elevated groundwater potential. This phenomenon can be attributed to its fine-textured alluvial deposits and level topography that facilitate both infiltration and groundwater retention (Nazir et al., 2024). Floodplains, despite occupying a lesser area (6.10%), also manifest extremely high groundwater potential. The coastal plain comprises 14.17% of the area and is characterized by moderate groundwater potential. Salt pans represent only 0.53% of the area and demonstrate minimal groundwater potential due to saline soil and poor infiltration capacity. Conversely, water bodies, although they represent only 1.54% of the total area, are classified as exhibiting very high groundwater potential.

Evaluating land use and land cover (LULC) is essential for understanding groundwater resources and sustainable management (Ramasubramanian et al., 2025). Land use and cover alterations significantly affect groundwater potential through their effect on recharge, groundwater levels, and water quality (Siddik et al., 2022; Salem et al., 2023). The LULC maps originate from Sentinel-2 Level-1C satellite imagery from 2023. The study area is characterized by cropland area (77.47%), built-up area (17.86%), water (2.27%), trees (0.64%), flooded vegetation (0.04%), Bare land (0.06%), and Range land (1.65%), as shown in Figure 3b. Cropland, water, trees, and flooded vegetation are optimal for recharge due to their facilitation of rainwater and irrigation water percolation, thus receiving greater weightage (Saravanan et al., 2020). In built-up areas, minimal weightage is observed due to increased runoff and reduced recharge (Kumar et al., 2024).

Geological factors critically influence groundwater potential by affecting its storage, flow, and replenishment (Pandey and Purohit, 2022). In the Tarangambadi Taluk study area, four prominent geological formations were delineated: palaeo marine deposits, palaeo fluvial deposits, active marine deposits, and active fluvial deposits, each possessing unique lithological and stratigraphic characteristics that influence groundwater behavior (Figure 3c). Palaeo marine deposits, comprising 27.58% of the region, consist of ancient sediments primarily formed from compacted clays and silts, exhibiting limited groundwater potential due to their fine-grained composition and low permeability (Faria et al., 2022). Conversely, Palaeo Fluvial deposits, which are 18.97%, are characterized by well-sorted sands and gravels that enhance porosity and permeability, thus facilitating significant groundwater recharge and functioning as crucial aquifer units (Sahoo et al., 2024). Active marine deposits, representing 46.69% of the area, consist of silty sands with moderate permeability, leading to a low to moderate groundwater potential, while active fluvial deposits, despite their smaller coverage of 6.76%, demonstrate exceptional groundwater resource potential due to their high recharge capacity from unconsolidated materials (Sud et al., 2023). Figure 3c presents the geological map of the research area.

Soil properties are critical for evaluating groundwater potential, as they affect infiltration and recharge rates (Arunbose et al., 2021). The interrelationship between soil and groundwater potential is governed by soil texture, structure, and hydraulic properties that affect groundwater infiltration and recharge (Muniraj et al., 2020; Vellaikannu et al., 2021). The soil group is classified into four categories: Entisols, Inceptisols, Vertisols, and Pondicherry soils, each uniquely influencing groundwater processes, as displayed in Figure 3d. Entisols, constituting approximately 37.60% of the study area, are relatively immature soil exhibiting minimal profile development, predominantly sandy to loamy in texture, which allows for moderate infiltration and drainage (Sathiyamoorthy et al., 2023). Inceptisols, representing 17.56%, are more developed than entisols, typically situated on alluvial plains, possessing superior structure and porosity that enhance vertical percolation and groundwater recharge (Loganathan and Sathiyamoorthy, 2024). Vertisols, the predominant soil type at 44.71%, are distinguished by their high clay content and shrink-swell characteristics (Raj et al., 2024; Loganathan and Sathiyamoorthy, 2024). Finally, a negligible 0.13% of the area consists of Pondicherry soil, which is coastal and often saline.

Precipitation significantly influences the assessment of groundwater potential, serving as the principal contributor to aquifer recharge (Thirunavukkarasu and Ambujam, 2020; Raj et al., 2024). The replenishment of groundwater through rainfall is contingent upon precipitation levels, soil moisture deficits, interception losses, and potential evaporation rates (Siddi Raju et al., 2019). The magnitude and temporal extent of precipitation significantly influence infiltration capacity and recharge potential (Muniraj et al., 2020). Prolonged low-intensity rainfall enhances infiltration and recharge, whereas short-duration, high-intensity precipitation leads to increased surface runoff and diminished recharge (Muniraj et al., 2020; Senapati and Das, 2022). In the study zone, average annual rainfall varying from 886.6 mm to 1398 mm is shown in Figure 4a. The rainfall map was classified into five categories: 886.6–976 mm (18.59%), 977–1,089 mm (16.03%), 1,090–1,199 mm (16.37%), 1,200–1,300 mm (20.42%), and 1,301–1,398 mm (28.59%).

The drainage density is crucial for evaluating groundwater potential, indicating surface runoff relative to infiltration (Francis et al., 2024). In the studied region, drainage density is classified into five categories, each representing a unique correlation with groundwater recharge capability. Areas with high drainage density (1.994–3.963 km/km²) constitute 2.35% of the region. High drainage density suggests steep terrains and impermeable surfaces, which elevate surface runoff and diminish infiltration (Guduru and Jilo, 2022). Similarly, a zone with moderately high drainage density (1.292–1.943 km/km²) covers 8.65% and aligns with inadequate groundwater recharge due to increased runoff (Uc Castillo et al., 2022). Conversely, regions with very low drainage density (0–0.2798 km/km²), which comprise 54.05% of the area, are characterized by mild slopes and permeable materials that facilitate infiltration and aquifer recharge (Melese and Belay, 2022). Low drainage density zones (0.2799–0.7927 km/km²) cover 15.59% of the area, while moderate drainage density values (0.7928–1.29 km/km²) represent 19.36% of the study region. Figure 4b shows the spatial distribution of the drainage density map.

Various geological formations encompassing faults, fractures, joints, and dykes serve as fundamental elements for the quantification of lineaments (Kanagaraj et al., 2019). The geological configuration of the lithosphere profoundly affects hydrological flow in both surface and subsurface environments (Hussein et al., 2017). A distinct correlation exists between the lineament density and zones of groundwater potential, as the lineament density regulates the volume of surface water that infiltrates aquifers (Sathiyamoorthy et al., 2023). An area characterized by a heightened lineament length density signifies substantial groundwater potential, suggesting an augmentation in secondary porosity (Kumar et al., 2024). The majority (90%) of areas are viable locations associated with diminished lineament density values, as exemplified in Figure 4c.

Areas closer to the stream generally have higher recharge capacity because water infiltration is more extensive, and greater influence on groundwater levels (Popalzai et al., 2023). Flat terrain where the groundwater level is higher than in another region, especially if it's closer to a stream network (Faheem et al., 2023). Water near the stream spends less time traveling through the subsurface before reaching the water-bearing layers. In the research zone, the location near the stream, approximately 392 m away, is classified as a very high-potential area, accounting for 36%. The distance of 833 m is classified as high potential, with 32% of the area falling under this category. The remaining 21 and 8% of areas come under moderate and low potential, respectively. Figure 4d shows the proximity to stream of the study area.

The intrusion of the Sea can compromise the quality of groundwater and limit its availability in coastal zones (Prusty and Farooq, 2020). Due to the minimal inland topographical variation and the progressive rise in sea levels, low-lying coastal areas exhibit heightened susceptibility (Zamrsky et al., 2024). When evaluating groundwater potential, incorporating Proximity to Sea habitats as an essential factor significantly enhances the utility of GIS-oriented MCDA-AHP techniques (Alshehri et al., 2024). Distance to sea is a crucial factor for evaluating groundwater potential assessment in coastal areas (Bhawan Faridabad, 2014). The Distance from the Sea varies from 0 to 20,700 m, as shown in Figure 4e. It is classified into five categories: 0 to 3621 m is very close to the sea with the highest risk of seawater intrusion, followed by 3,622 m to 7,400 m, which has a moderate risk of seawater intrusion. Ranges that 41.45% of the area studied fall under the very high to high risk of seawater intrusion, which indirectly affects the potential areas.

The slope is one of the fundamental units of assessing groundwater potential because it directly affects the surface runoff and water level (Babu and Maury, 2025). A steep slope reduces water recharge, while a lower slope enhances recharge (Kumar et al., 2024). The slope has a positive correlation with surface runoff but a negative correlation with surface water infiltration and percolation (Ghosh, 2021). In areas that seep deeper into the earth, eventually raising groundwater levels. The slope ranges from 0 to 12.82 degrees, as shown in Figure 4f. In the study area, nearly 66% of the area contributes a high yield of groundwater potential, which is a more flat or plain surface. The remaining 21.52 and 12.48% have moderate and low yield capacity for groundwater potential, respectively.

The Topographic Wetness Index (TWI) is derived from the catchment area in conjunction with the terrain of slope, thereby yielding crucial insights into regions where water accumulation is probable (Hassaballa and Salih, 2024). TWI generally exhibits a gradient from minimal value in the areas characterized by adequate drainage (for instance, ridges and steep slopes) to elevated value in areas with inadequate drainage (such as valleys and flat plains) (Tripathi et al., 2025). The TWI values range from 4.881 to 22.07, as shown in Figure 5a, which is common in natural landscapes. The TWI values indicate that 65.9% of the area lies in a low potential area, and 16.39% of the area contributes to a high potential area. TWI is calculated using this Equation 4.

Where, A_s denoted as the area of boundary, and tan (β) represents a specific grid of local slope angle.

The gradation of landforms at topographical slope sites is conventionally quantified and automated through the application of the Topographic Position Index (TPI) methodology (Fatema et al., 2023). Elevated TPI values characterize elevated terrains such as hilltops and ridges, whereas valleys and depressions exhibit diminished TPI values; conversely, flat or mid-slope areas are associated with values that approximate zero (Arulbalaji et al., 2019). Given their capacity to capture surface runoff and facilitate infiltration, thereby enhancing groundwater recharge, regions with low TPI values, such as valleys and flat terrains, are generally associated with increased groundwater potential (Pillai et al., 2023). The TPI value ranges from −3.6 to 5.222, shown in Figure 5b, with 35.82% of the area contributing high potential and 40% of the area being moderate, which has a near-zero value, representing a flat or plain terrain. Only 23.6% of the area has gentle ridges and peaks in the research area.

The normalized difference vegetation index (NDVI) yields vital insights into the moisture levels in the soil and the coverage of vegetation, both of which greatly influence the areas marked as having potential groundwater resources (Ravindran et al., 2024). Furthermore, beyond groundwater availability, different factors that could influence vegetation robustness, including soil moisture levels and modifications in land use, might also affect the NDVI (Babu and Maury, 2025). The NDVI ranges from −0.1432 to 0.5979, as shown in Figure 5c. The NDVI classification shows that 46.7% of the areas contribute low potential yields, and 31.73% of the regions contribute high potential yields. NDVI was identified using Equation 5.

Determining groundwater potential is contingent upon the Normalized Difference Water Index (NDWI), a calculation obtained through remote sensing technology that measures how much water is contained in terrestrial soil and vegetation (Borah and Bora, 2025). This approach proves particularly helpful in regions where the recharge of groundwater may be significantly influenced by the extent of vegetation cover (Sulle et al., 2023). To enhance the accuracy of groundwater potential, these factors have supplementary environmental and geographic variables. The NDWI ranges from −0.51 to 0.18 (Figure 5d), closer to the 0.186 value, and contributes directly to groundwater recharge, which contains more moisture content and 8.6% of the area. NDWI was determined by using Equation 6.

The incorporation of coastal dynamics provides a critical insight into the groundwater system of Tarangambadi Taluk. Regions situated in close proximity to sea environments and low-lying coastal geomorphic features were recognized as areas more susceptible to seawater intrusion and diminished recharge capacity. In contrast, inland coastal alluvial deposits and beach ridge formations demonstrate comparatively superior groundwater potential owing to conductive infiltration conditions. Through the integration of proximity to sea and coastal characteristics, the analysis underscores the extent to which the sea significantly influences and modifies the groundwater potential in coastal aquifers. This innovative focus on coastal dynamics serves to differentiate the current study from other inland groundwater potential studies and thereby augments the rigor of the identified zones.

AHP methods assigned weights and ranks to fourteen thematic layers for the detection of the Groundwater Potential Zone (GWPZ). The layers were rasterised at a 30 ^* 30 m cell size and combined with weights and ranks using ArcGIS 10.8 (Abijith et al., 2020). The weighted overlay analysis's final output was generated by integrating all layers in the ArcGIS raster calculator, categorizing results into five very low, low, moderate, high, and very high) to create the GWPZ map. The delineated Groundwater Potential Zone (GWPZ) Map, displayed in Figure 6, offers a visual representation of the spatial heterogeneity concerning groundwater resources throughout the study region (Dar et al., 2021). Table 6 delineates the categorization of the research area according to groundwater potential and the associated spatial extent of each classification. Very high potential zones constitute 26.52% of the total area (74.51 km²), primarily dominating the western and southwestern regions of the study area, which exhibit advantageous hydrogeological conditions such as floodplains, active fluvial deposits, and low-gradient agricultural terrains. These regions are characterized by elevated infiltration rates and permeable soils, which are conducive to groundwater recharge. High potential zones encompass 25.30% (71.09 km²). This is particularly evident within the central corridor areas, which demonstrate moderate to high recharge potential. These zones may overlap with agricultural lands or geomorphological features such as coastal plains that possess sufficient permeability.

Moderate Potential zones represent the most significant proportion, spanning 29.49% (82.86 km²). These zones constitute the central spine and portions of the northern area, typifying transitional regions with a blend of moderate infiltration capacities and drainage characteristics. These areas may include diverse geomorphic units such as coastal plains and soils with moderate drainage. Low and Very Low zones collectively account for 18.69% of the study area (52.52 km²), primarily concentrated in the eastern and southeastern regions, particularly in Proximity to the coastline. These zones are predominantly characterized by impervious formations, urbanized surfaces, or salt-affected soils (e.g., paleo-marine deposits), which hinder infiltration. Furthermore, the Proximity to Sea may pose a risk of saline intrusion, rendering these zones vulnerable and less appropriate for groundwater extraction without the implementation of desalination or recharge facilities.

This distribution indicates that over 50% of the study area demonstrates high to very high groundwater potential, presenting opportunities for sustainable groundwater development. However, areas with low and very low potential necessitate careful management for extraction, warranting the implementation of recharge strategies to enhance their viability.

Validation ascertains the GWPZ map's accurate representation of groundwater potential, which is imperative for sustainable resource management (Mihret and Wuletaw, 2025). A rigorously validated GWPZ map facilitates informed decision-making by supplying reliable data for conservation and utilization strategies (Duguma and Duguma, 2022). The validation datasets were procured from the State Ground and Surface Water Resources Data Centre, Tamil Nadu, covering the period from January 1991 to December 2024, with monthly records for each well. For validation purposes, we used the 34-year average annual water level for each well. This approach integrates both seasonal (pre-monsoon and post-monsoon) and interannual variability into a single representative value. By utilizing long-term averages, the validation outcomes are rendered less susceptible to the effects of anomalous wet or dry years, consequently providing a more robust foundation for comparison with the anticipated groundwater potential zones. To ensure GWPZ accuracy, data from 13 dug wells and Tube wells were cross-validated. Actual water levels from study area wells were compared to predicted groundwater potential index values for consistency (Loganathan and Sathiyamoorthy, 2024). To evaluate groundwater availability, long-term average water levels from the validated observation wells in Tarangambadi Taluk were categorized into High, Medium, and Low Potential. This classification utilized the equal interval method, based on water levels ranging from 2.36 to 14.21 mbgl (measured in meters below groundwater level, mbgl). High Groundwater Potential zones are categorized by shallow water levels (2.36–6.31 mbgl), signifying favorable groundwater access and enhanced recharge capacity. Medium Groundwater Potential areas range from 6.32–10.26 mbgl, indicating intermediate water table depths. Low Groundwater Potential zones, with depths of 10.27–14.21 mbgl, denote deeper water tables and possible limitations in recharge or risks of over-extraction. Most wells (10 out of 13) exhibited High Potential, reflecting prevalent shallow water levels and robust recharge conditions. The validation of the groundwater potential map accuracy was conducted through observed groundwater level data, with the findings encapsulated in Table 7. A strong correlation was observed in 10 out of 13 wells between actual groundwater conditions and predicted potential classes, with three wells showing discrepancies.

Total Wells count = 13

actual groundwater level data and predicted, same agreement, wells count = 10

actual groundwater level data and predicted, varying agreement, wells count = 3

groundwater potential accuracy = (10/13) ^* 100 = 76.9%.

The accuracy of the GWPZ map was determined to be 76.92%. The findings point out the effectiveness of integrating the Analytical Hierarchy Process (AHP), Remote Sensing (RS), and Geographic Information System (GIS) for accurately assessing groundwater prospects in coastal agricultural sectors, including Tarangambadi Taluk.